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Quality improvement can be realized by measurable reductions in cost, errors, or risk, improved health indicators for individuals and populations, and increased patient satisfaction. Healthcare systems and processes are subject to variation due to factors such as the inherent differences in patients, operational practices and procedures, clinician skill and training, and facilities and equipment. Improvements can be made by reducing variation. For example, hospitalized patient satisfaction can be raised and food waste reduced when meals are delivered as scheduled. Achieving improvement requires identifying and understanding the many sources of variation that can affect process performance. For example, timely hospital patient discharge can be affected by variations in staffing levels, pharmacy fulfillment times, demand for beds, etc.

A key step in quality improvement is to create a map or diagram that shows the sequence of the main process steps. Figure 1.1 shows a high‐level process map for the preoperative total joint replacement (TJR) process, which will be the subject of three chapters of this book that illustrate the lifecycle of a process improvement initiative.

Figure 1.1 Process map for the preoperative total joint replacement.

Such process maps are useful for bounding the scope of the project. Process maps with more detail are good for identifying sources of variability and whether or not these sources of variation are controllable by the organization. Understanding which variability sources are controllable and which are not helps in defining potential improvement actions that an organization can undertake. For example, in Figure 1.1, process step 3 (when the patient meets with the primary care provider), is mostly outside of the control of the hospital, whereas process step 2 (book preoperative appointment) can be changed and is more likely to yield process improvements because patients' preoperative appointment scheduling takes place at the orthopedic clinic.

Sources of variation are also classified as common‐cause or special‐cause. Common cause variation is inherent in the process and reducing this type of variation, requires a change in the process itself. For example, the variation in the time between process steps 2 (preoperative appointment) and 3 (the preoperative clearance) is between two and four days for knee or hip replacements and seems to be reasonable variation for this part of the process. As such, this would be considered common cause variation. However, the variation in the time between the preoperative appointment and the preoperative clearance can be as high as 40 business days. This unusual variation is attributed to special cause, which arises from unusual circumstances. The variability for the Conformis‐brand prosthetic knee replacement process is explained by additional preoperative steps, which not only require extra studies such an magnetic resonance imaging (MRI) but also the fabrication of a prosthetic by a vendor. If there is a problem with the prosthetic, the process takes longer than expected. This special circumstance leads to longer elapsed times than usual, and hence greater process variability that is outside the control of the clinic, adversely affecting the process performance. Identifying variation as either common‐cause or special‐cause can assist in developing and prioritizing potential improvement actions. In this casebook, we present tools for assessing variability both graphically and numerically. Data visualization and data slicing (or subgrouping) are powerful methods for identifying and quantifying process variation. Additional discussion on variability in the context of quality improvement can be found in Deming (1986) and Hoerl and Snee (2012).